Optimal trajectory planning by Big Bang-Big Crunch algorithm

Sabri Yilmaz, Metin Gokasan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

Path planning is an interesting topic which is affected by lots of variables, as: time, energy, torque and stability. In this study, a new method based on Big Bang-Big Crunch algorithm is proposed to find optimum values of the parameters of a path and a cost function in order to minimize applied torque and tracking error. For this purpose the mathematical model of the manipulator is derived with mainly used methods, Denavit-Hartenberg, Jacobian and Euler-Lagrange methods. By using classical robot modeling methods, Big Bang-Big Crunch algorithm searched for the optimum trajectory and found the optimum value of the cost function.

Original languageEnglish
Title of host publicationProceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014
EditorsImed Kacem, Pierre Laroche, Zsuzsanna Roka
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages557-561
Number of pages5
ISBN (Electronic)9781479967735
DOIs
Publication statusPublished - 23 Dec 2014
Event2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014 - Metz, France
Duration: 3 Nov 20145 Nov 2014

Publication series

NameProceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014

Conference

Conference2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014
Country/TerritoryFrance
CityMetz
Period3/11/145/11/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • Big Bang-Big Crunch optimization algorithm
  • Path planning
  • Robot dynamics
  • Robot kinematics

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